{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "\n",
    "sys.path.append('../..')\n",
    "import torchdyn; from torchdyn.models import *; from torchdyn.datasets import *\n",
    "from pytorch_lightning.loggers import WandbLogger"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f063ebb1ad0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = ToyDataset()\n",
    "n_samples = 1 << 16\n",
    "n_gaussians = 7\n",
    "\n",
    "X, yn = data.generate(n_samples // n_gaussians, 'gaussians', n_gaussians=7, std_gaussians=0.1, dim=2, radius=2)\n",
    "X = (X - X.mean())/X.std()\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(3, 3))\n",
    "plt.scatter(X[:,0], X[:,1], c='orange', alpha=0.3, s=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.utils.data as data\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "X_train = torch.Tensor(X).to(device)\n",
    "y_train = torch.LongTensor(yn).long().to(device)\n",
    "train = data.TensorDataset(X_train, y_train)\n",
    "trainloader = data.DataLoader(train, batch_size=512, shuffle=True) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.distributions import MultivariateNormal, Uniform, TransformedDistribution, SigmoidTransform, Categorical\n",
    "prior = MultivariateNormal(torch.zeros(2).to(device), torch.eye(2).to(device))\n",
    "\n",
    "f = nn.Sequential(        \n",
    "        nn.Linear(2, 32),\n",
    "        nn.Softplus(),\n",
    "        DataControl(),\n",
    "        nn.Linear(32+2, 2)\n",
    "    )\n",
    "\n",
    "# cnf wraps the net as with other energy models\n",
    "noise_dist = MultivariateNormal(torch.zeros(2).to(device), torch.eye(2).to(device))\n",
    "\n",
    "cnf = nn.Sequential(CNF(f))\n",
    "\n",
    "nde = NeuralDE(cnf, solver='dopri5', s_span=torch.linspace(0, 1, 2), atol=1e-6, rtol=1e-6, sensitivity='adjoint')\n",
    "\n",
    "model = nn.Sequential(Augmenter(augment_idx=1, augment_dims=1),\n",
    "                      nde).to(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Learner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cnf_density(model):\n",
    "    with torch.no_grad():\n",
    "        npts = 200\n",
    "        side = np.linspace(-2., 2., npts)\n",
    "        xx, yy = np.meshgrid(side, side)\n",
    "        memory= 100\n",
    "\n",
    "        x = np.hstack([xx.reshape(-1, 1), yy.reshape(-1, 1)])\n",
    "        x = torch.from_numpy(x).type(torch.float32).to(device)\n",
    "\n",
    "        z, delta_logp = [], []\n",
    "        inds = torch.arange(0, x.shape[0]).to(torch.int64)\n",
    "        for ii in torch.split(inds, int(memory**2)):\n",
    "            z_full = model(x[ii]).cpu().detach()\n",
    "            z_, delta_logp_ = z_full[:, 1:], z_full[:, 0]\n",
    "            z.append(z_)\n",
    "            delta_logp.append(delta_logp_)\n",
    "\n",
    "        z = torch.cat(z, 0)\n",
    "        delta_logp = torch.cat(delta_logp, 0)\n",
    "\n",
    "        logpz = prior.log_prob(z.cuda()).cpu() # logp(z)\n",
    "        logpx = logpz - delta_logp\n",
    "        px = np.exp(logpx.cpu().numpy()).reshape(npts, npts)\n",
    "        plt.imshow(px);\n",
    "        plt.xlabel([])\n",
    "        plt.ylabel([])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Learner(pl.LightningModule):\n",
    "    def __init__(self, model:nn.Module):\n",
    "        super().__init__()\n",
    "        self.model = model\n",
    "        self.lr = 1e-3\n",
    "        \n",
    "    def forward(self, x):\n",
    "        return self.model(x)\n",
    "    \n",
    "    def training_step(self, batch, batch_idx):\n",
    "        # plot logging  \n",
    "        if batch_idx == 0: \n",
    "            cnf_density(self.model)\n",
    "            self.logger.experiment.log({\"chart\": plt})\n",
    "            plt.close()\n",
    "            nde.nfe = 0\n",
    "        \n",
    "        x, _ = batch     \n",
    "        x += 1e-2*torch.randn_like(x).to(x)\n",
    "        xtrJ = self.model(x)  \n",
    "        logprob = prior.log_prob(xtrJ[:,1:]).to(x) - xtrJ[:,0] \n",
    "        loss = -torch.mean(logprob)\n",
    "        \n",
    "        nfe = nde.nfe\n",
    "        nde.nfe = 0\n",
    "        \n",
    "        metrics = {'loss': loss, 'nfe':nfe}\n",
    "        self.logger.experiment.log(metrics) \n",
    "        return {'loss': loss}   \n",
    "    \n",
    "    def configure_optimizers(self):\n",
    "        return torch.optim.AdamW(self.model.parameters(), lr=self.lr, weight_decay=1e-5)\n",
    "\n",
    "    def train_dataloader(self):\n",
    "        self.loader_l = len(trainloader)\n",
    "        return trainloader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: True, used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "CUDA_VISIBLE_DEVICES: [0]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
       "                Project page: <a href=\"https://app.wandb.ai/zymrael/torchdyn-toy_cnf-bench\" target=\"_blank\">https://app.wandb.ai/zymrael/torchdyn-toy_cnf-bench</a><br/>\n",
       "                Run page: <a href=\"https://app.wandb.ai/zymrael/torchdyn-toy_cnf-bench/runs/t47srcaf\" target=\"_blank\">https://app.wandb.ai/zymrael/torchdyn-toy_cnf-bench/runs/t47srcaf</a><br/>\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "  | Name  | Type       | Params\n",
      "-------------------------------------\n",
      "0 | model | Sequential | 166   \n",
      "/home/jyp/michael_dev/testenv/lib/python3.7/site-packages/pytorch_lightning/utilities/distributed.py:25: UserWarning: The dataloader, train dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 20 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
      "  warnings.warn(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ea09c6bab3394c42894ca24d36ea70aa",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Training', layout=Layout(flex='2'), max…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jyp/michael_dev/testenv/lib/python3.7/site-packages/pytorch_lightning/utilities/distributed.py:25: UserWarning: Detected KeyboardInterrupt, attempting graceful shutdown...\n",
      "  warnings.warn(*args, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "logger = WandbLogger(project='torchdyn-toy_cnf-bench')\n",
    "learn = Learner(model)\n",
    "trainer = pl.Trainer(min_steps=45000, max_steps=45000, gpus=1, logger=logger)\n",
    "trainer.fit(learn);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample = prior.sample(torch.Size([1<<15]))\n",
    "# integrating from 1 to 0, 8 steps of rk4\n",
    "model[1].s_span = torch.linspace(0, 1, 2)\n",
    "new_x = model(sample).cpu().detach()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cnf_density(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f062fa51110>"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 4))\n",
    "plt.subplot(121)\n",
    "plt.scatter(new_x[:,1], new_x[:,2], s=0.3, c='blue')\n",
    "#plt.scatter(boh[:,0], boh[:,1], s=0.3, c='black')\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.scatter(X[:,0], X[:,1], s=0.3, c='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def cnf_density(model):\n",
    "    with torch.no_grad():\n",
    "        npts = 200\n",
    "        side = np.linspace(-2., 2., npts)\n",
    "        xx, yy = np.meshgrid(side, side)\n",
    "        memory= 100\n",
    "\n",
    "        x = np.hstack([xx.reshape(-1, 1), yy.reshape(-1, 1)])\n",
    "        x = torch.from_numpy(x).type(torch.float32).to(device)\n",
    "\n",
    "        z, delta_logp = [], []\n",
    "        inds = torch.arange(0, x.shape[0]).to(torch.int64)\n",
    "        for ii in torch.split(inds, int(memory**2)):\n",
    "            z_full = model(x[ii]).cpu().detach()\n",
    "            z_, delta_logp_ = z_full[:, 1:], z_full[:, 0]\n",
    "            z.append(z_)\n",
    "            delta_logp.append(delta_logp_)\n",
    "\n",
    "        z = torch.cat(z, 0)\n",
    "        delta_logp = torch.cat(delta_logp, 0)\n",
    "\n",
    "        logpz = prior.log_prob(z.cuda()).cpu() # logp(z)\n",
    "        logpx = logpz - delta_logp\n",
    "        px = np.exp(logpx.cpu().numpy()).reshape(npts, npts)\n",
    "        plt.imshow(px, cmap='inferno', vmax=px.mean());\n",
    "a = cnf_density(model)       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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